On the Potential of CLIP for Compositional Logical Reasoning
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
In this paper we explore the possibility of using OpenAI's CLIP to perform logically coherent grounded visual reasoning. To that end, we formalize our terms and give a geometric analysis of how embeddings in CLIP's latent space would need to be configured in order for the system to be logically coherent. Our main conclusion is that, as usually configured, CLIP cannot perform such reasoning.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 5 of its 12 references that are in the map. First posted on arXiv 30 Aug 2023.
Builds on
5 of 12 references.
References · 5
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
- 2015VQA: Visual Question Answeringcited by 2,506
Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning
- 2026ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP
- 2022A Benchmark for Compositional Visual Reasoning
- 2026MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning
- 2025Explain Before You Answer: A Survey on Compositional Visual Reasoning
- 2025NAVER: A Neuro-Symbolic Compositional Automaton for Visual Grounding with Explicit Logic Reasoning
- 2022CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks
- 2024Take A Step Back: Rethinking the Two Stages in Visual Reasoning